MMWS: Stata module to perform marginal mean weighting through stratification

Ariel Linden

Statistical Software Components from Boston College Department of Economics

Abstract: mmws implements a method, originally proposed by Huang et al (2005), extended by Hong (2010, 2012), and further described by Linden (2014) that combines elements of two propensity score-based techniques, stratification and weighting. mmws is a data pre-processing procedure that reweights a dataset to balance the observed pretreatment characteristics across all treatment groups. Under the ignorability assumption, the weighted data should approximate a randomized experiment. The weights generated by mmws can then be passed to the appropriate outcome model for use in subsequent analyses. Before using mmws, the propensity score for a given treatment must be estimated. A logit or probit regression model can be used for estimating the propensity score for a binary treatment; an ordinal logit or probit regression model can be used to estimate the propensity score for a treatment with ordered levels (e.g. varying doses of a drug); and a multinomial logistic or probit regression can be used for estimating propensity scores for nominal (independent) treatments.

Language: Stata
Requires: Stata version 11
Keywords: propensity score; stratification; weighting; marginal mean weighting; causal inference (search for similar items in EconPapers)
Date: 2014-08-15, Revised 2026-09-15
Note: This module should be installed from within Stata by typing "ssc install mmws". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/m/mmws.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/m/mmws.sthlp help file (text/plain)

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